Objective：To explore the epidemiological status of sepsis - associated myocardial injury（SAMI）and its impact on prognosis，and to construct a nomogram for early identification of high - risk groups of SAMI. Methods：A retrospective study was conducted to collect clinical data of sepsis patients hospitalized in the Department of Emergency Medicine，the First Affiliated Hospital of Nanjing Medical University from July 2023 to December 2024. The incidence of SAMI was analyzed，and 28- day Kaplan -Meier survival curves were drawn to compare the impact of SAMI on the prognosis of sepsis. Clinical variables were screened by least absolute shrinkage and selection operator（LASSO）regression and Boruta algorithm，respectively. Multivariate logistic regression analysis was used to construct the early prediction model of SAMI. Results：A total of 353 patients with sepsis were included，of whom 195（55.2%）developed SAMI during the course of the disease. The 28- day mortality risk was significantly higher in patients with SAMI than in patients without SAMI（HR=2.342，P ＜ 0.001）. By using LASSO regression and Boruta algorithm，variables were screened and intersections were taken. Finally，6 variables including age，history of coronary heart disease，creatinine，urea nitrogen， D-dimer and procalcitonin were constructed and nomogram was drawn. The area under receiver operating characteristic curve of the internal validation using the bootstrap method（resampling=1 000）was 0.770（95%CI：0.767-0.773，P ＜ 0.001）. The calibration curve fitted well，and the decision curve analysis showed that the prediction model had a good net benefit in the range of threshold probability 0-0.95. Conclusion：SAMI is a common complication of sepsis and leads to poor prognosis. Nomogram based on clinical variables has a good clinical application prospect.